PTPN Knockout
Kristen Wells
August 01, 2022
source("src/rules/scripts/functions.R")
################
# Theme colors #
################
# Colors for heatmap (from the ArchR package)
blueYellow <- c("#352A86", "#343DAE", "#0262E0", "#1389D2", "#2DB7A3",
"#A5BE6A", "#F8BA43", "#F6DA23", "#F8FA0D")
# Colors for statistics
fastqc_colors <- c("#e31a1c", "#238443", "#ec7014")
star_colors <- c("#737373", "#8c6bb1", "#ec7014",
"#e31a1c", "#238443", "#225ea8")Background
This documents the analysis of the effect of cytokine treatment on PTPN2 KO mice. This document aims to analyze differences between PTPN2 KO and WT mice in their response to cytokine treatment.
# Load in the files
###############
# Count table #
###############
# Read in the count table
count_table <- read.table(params$counts, sep = "\t", row.names = 1, header = T)
################
# Sample table #
################
# Read in the sample table
sample_table <- read.table(params$sample_info, sep = ",", header = T,
row.names = 1)
# Ensure the count table and sample table are in the same order
count_table <- count_table[ , rownames(sample_table)]
########
# Star #
########
# Read in star stats
star_stats <- read.table(params$star_stats, sep = "\t", row.names = 1,
header = T)
# Filter for mapping % and total read counts
star_stats <- star_stats[grepl(
"%|Uniquely mapped reads|mapped to multiple loci",
rownames(star_stats)), ]
star_stats <- star_stats[!grepl(
"^Mismatch|chimeric", rownames(star_stats)), ]
# Calculate median size
star_stats_median <- star_stats[grepl(
"Uniquely mapped reads", rownames(star_stats)), ]
median_size <- median(as.numeric(
star_stats_median["Uniquely mapped reads number", ])) / 1000000
median_rate <- median(as.numeric(str_remove(
star_stats_median["Uniquely mapped reads %", ], "%")))
alignment_qual <- get_alignment_qual(median_rate)
# Pull out all sample names
samples <- colnames(star_stats)
# Make a column of metrics
star_stats$metric <- rownames(star_stats)
# Make into long form for ggplot
star_stats_long <- gather(star_stats, key = "Sample", value = "value",
all_of(samples))
# Add column for value type
star_stats_long <- star_stats_long %>%
mutate(
value = as.numeric(str_remove(value, "%")),
val_type = ifelse(grepl("%", metric),
"pct", "int")
)
# Pull out read counts
# Calculate median library size
read_counts <- star_stats_long %>%
filter(grepl("mapped reads number", metric)) %>%
mutate(value = round(value / 1000000)) %>%
mutate(value = str_c(value, " million"))
##########
# FastQC #
##########
# Load in fastqc results
fastqc_summary <- read.table(params$fastqc, sep = "\t")
# Change the colnames
colnames(fastqc_summary) <- c("Result", "Test", "Sample")
# Update sample names
fastqc_summary$Sample <- sub("_S[0-9]*_L[0-9]*_", "", fastqc_summary$Sample)
fastqc_summary$Sample <- sub("_001.fastq.gz", "", fastqc_summary$Sample)
##############
# Set colors #
##############
sample_table[[params$sample_column]] <-
factor(sample_table[[params$sample_column]])
sample_colors <- brewer.pal(length(
levels(sample_table[[params$sample_column]])), "Set1")
names(sample_colors) <- levels(sample_table[[params$sample_column]])Quality Control
FastQC Summary
FastQC was used to assess the quality of each fastq
file. A summary of the results is shown below, the overall library
quality was ****.
fastqc_plot <- ggplot(data = fastqc_summary,
mapping = aes(x = Sample,
y = Test,
fill = Result)) +
geom_tile(color = "white", size = 0.5) +
scale_fill_manual(values = fastqc_colors) +
theme_classic() +
theme(
legend.title = element_blank(),
legend.text = element_text(size = 8, color = "black"),
axis.title = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
)
fastqc_plot
Alignment Summary
Reads were aligned to the GRCm38 genome using the STAR
RNA-seq aligner. A summary of the results is shown below, the library
size is displayed on each bar. The median library size was 64.9422335
million reads and the median alignment rate was 92.36. Overall, the
alignment rate was high.
# Plot alignment stats
star_stats_long_pct <- star_stats_long %>%
filter(val_type == "pct") %>%
# Add median rates to metric label
mutate(
metric = str_remove(metric, "% of reads | reads %"),
metric = str_to_sentence(metric)
) %>%
arrange(value) %>%
mutate(metric = fct_inorder(metric))
# Plot STAR alignment stats
star_plot <- ggplot(data = star_stats_long_pct,
mapping = aes(x = Sample,
y = value,
fill = metric)) +
geom_bar(stat = "identity", color = "white", size = 0.5) +
# Label bars with library size
geom_text(
data = read_counts,
aes(Sample, 50, label = value),
show.legend = F,
inherit.aes = F,
color = "white",
angle = 90
) +
scale_fill_manual(
values = star_colors,
guide = guide_legend(reverse = T)
) +
scale_y_continuous(labels = function(x) {str_c(x, "%")}) +
theme_classic() +
theme(
legend.title = element_blank(),
legend.text = element_text(size = 8, color = "black"),
axis.title = element_blank(),
axis.text = element_text(size = 8, color = "black"),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)
)
star_plot
PCA
Principal component analysis was performed to assess similarities between samples. Biological replicates were plotted for each experimental condition. When replicates cluster close together, this suggests that overall gene expression patterns are fairly reproducible.
# This makes a DESeq2 object where the count data is our count matrix and the colData is our sample data. I am currently making the design based on "group" but we can change this if necessary
sample_table$genotype <- factor(sample_table$genotype,
levels = c("PTPN2_KO", "WT"))
sample_table$treatment <- factor(sample_table$treatment,
levels = c("Cytokine_Treatment", "No_Treatment"))
sample_table$group <- factor(sample_table$group,
levels = c("PTPN2_KO_Cytokine_Treatment",
"PTPN2_KO_NoTreatment",
"WT_Cytokine_Treatment",
"WT_NoTreatment"))
dds <- DESeqDataSetFromMatrix(countData = count_table,
colData = sample_table,
#design = formula(paste("~",
# params$sample_column))
design = ~genotype + treatment +
genotype:treatment)
# Here we get rid of all the genes that are not expressed
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds <- DESeq(dds)
# Create a model
mod_mat <- model.matrix(design(dds), colData(dds))
PTPN2_non <- colMeans(mod_mat[dds$genotype == "PTPN2_KO" &
dds$treatment == "No_Treatment", ])
PTPN2_Cyto <- colMeans(mod_mat[dds$genotype == "PTPN2_KO" &
dds$treatment == "Cytokine_Treatment", ])
WT_Non <- colMeans(mod_mat[dds$genotype == "WT" &
dds$treatment == "No_Treatment", ])
WT_Cyto <- colMeans(mod_mat[dds$genotype == "WT" &
dds$treatment == "Cytokine_Treatment", ])
# This transforms the counts to have constant variance. This helps ensue that highly or lowly expressed genes will not contribute too much for the PCA or clustering analysis
vsd <- vst(dds, blind = F)
plot_pca(vsd, group_by = params$sample_column, color_palette = sample_colors)
Sample Distances
Correlations between samples was determined to further assess similarities between samples. Biological replicates should cluster together. Samples that are more similar to each other have values closer to 0.
# This determines distance between samples.
get_distances(vsd)
Differential Expression Analysis
PTPN2_WT_noTrt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
126 differentially expressed genes were identified
using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
WT_PTPN2_noTrt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
126 differentially expressed genes were identified
using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
WT_trt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
12,347 differentially expressed genes were
identified using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
PTPN2_trt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
12,762 differentially expressed genes were
identified using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
PTPN2_WT_Trt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
659 differentially expressed genes were identified
using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
PTPN2_specific_trt
# Extract DE genes
if(interaction){
sig_genes <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, de_test = de_test,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
sig_genes_all <- get_de(dds, de_test = de_test, interaction = interaction,
var1 = treatment, var2 = control,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
} else {
sig_genes <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = params$DE_alpha,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = sect_title)
sig_genes_high_lfc <- get_de(dds, column = params$sample_column,
var1 = control, var2 = treatment,
interaction = interaction, write_csv = F,
p_value = params$DE_alpha,
lfc = params$DE_lfc,
lfc_shrink = T)
all_genes_all <- get_de(dds, column = params$sample_column, var1 = treatment,
var2 = control, interaction = interaction,
write_csv = T, p_value = 1,
lfc = 0, output_dir = params$output_dir,
lfc_shrink = T, save_name = paste0(sect_title, "_all"))
}
DE_genes_n <- nrow(sig_genes$DE_genes)DEseq2 analysis
181 differentially expressed genes were identified
using the DESeq2 package.
Differentially expressed genes are labeled on the MA plot in blue.
plotMA(sig_genes$DE_df, main = sect_title)
Heatmaps
Heatmap of DE genes across all samples
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = TRUE,
output_dir = heatmap_dir,
color_test = sample_colors,
save_name = sect_title)
print(heatmap)
Heatmap of DE genes across only the samples used in the comparision
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
heatmap <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = sig_genes$DE_genes,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = FALSE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap)
Gene Set Enrichment
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
evcode <- TRUE
result <- run_gprofiler_all(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir, "GSE_files"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
save_name = sect_title,
evcodes = evcode)
plots <- result$plots
gost_table <- result[[paste0(treatment, "_vs_", control)]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG")
gost_file <- openxlsx::createWorkbook()
invisible(lapply(save_gost, function(x){
save_table <- gost_table %>%
dplyr::filter(source == x)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", x)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}))
openxlsx::saveWorkbook(wb = gost_file,
file = paste0(params$output_dir, "/GSE_files/",
sect_title,
"_GSE.xlsx"),
overwrite = TRUE)
plot_grid(plots$GOBP, plots$GOMF, plots$GOCC, plots$KEGG,
nrow = 4,
ncol = 1,
align = "v",
axis = "tb")Gene Set Enrichment Separating Up and Down Regulation
For Gene Set Enrichment, I used gprofiler2 It uses a
hypergeometric test to determine overrepresentation of genes from
different categories.
all_gene_ens <- sub(".*_ENSMUSG", "ENSMUSG", rownames(dds))
ifelse(!dir.exists(file.path(params$output_dir, "GSE_files_separated")),
dir.create(file.path(params$output_dir, "GSE_files_separated")),
FALSE)[1] FALSEresult <- run_gprofiler(gene_table = sig_genes$DE_genes,
pos_name = treatment,
neg_name = control,
custom_bg = all_gene_ens,
save_dir = file.path(params$output_dir,
"GSE_files_separated"),
plot_dir = file.path(params$output_dir, "images",
"GSE"),
evcodes = FALSE)
plots <- result$plots
gost_table_trt <- result[[treatment]]$result
gost_table_ctl <- result[[control]]$result
save_gost <- c("GO:BP", "GO:CC", "GO:MF", "KEGG", "WP")
gost_file_trt <- openxlsx::createWorkbook()
gost_file_ctl <- openxlsx::createWorkbook()
add_gost_data <- function(gost_table, gost_file, source_test,
evcode = FALSE){
save_table <- gost_table %>%
dplyr::filter(source == source_test)
if(evcode){
save_table <- save_table %>%
dplyr::select(!c(intersection, evidence_codes))
}
sheetname <- sub(":", "_", source_test)
openxlsx::addWorksheet(wb = gost_file, sheetName = sheetname)
openxlsx::writeData(wb = gost_file, sheet = sheetname, x = save_table)
}
invisible(lapply(save_gost, function(x){
add_gost_data(gost_table_trt, gost_file_trt, x, evcode = FALSE)
add_gost_data(gost_table_ctl, gost_file_ctl, x, evcode = FALSE)
}))
openxlsx::saveWorkbook(wb = gost_file_trt,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", treatment,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
openxlsx::saveWorkbook(wb = gost_file_ctl,
file = file.path(params$output_dir, "GSE_files_separated",
paste0(sect_title, "_", control,
"_upregulated_genes_GSE.xlsx")),
overwrite = TRUE)
plot_grid(plots$C_GOBP, plots$T_GOBP,
plots$C_GOMF, plots$T_GOMF,
plots$C_GOCC, plots$T_GOCC,
plots$C_KEGG, plots$T_KEGG,
nrow = 8,
ncol = 1,
align = "v",
axis = "tb")
Gene plots
Gene plots show the normalized expression of key genes across all samples. We can add in any genes that you would like to see in this format.
Ins1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Ins2
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Gbp2
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Gbp5
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Cxcl1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Pomc
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Chl1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Npy
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Il33
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Robo1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Rnasel
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Gbp8
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Serpina3i
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Raet1e
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Ggt1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Apol7a
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Pcyt1b
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Rps3a1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Bmp7
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Mapk13
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Sec22c
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Clu
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Pink1
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Lrrtm4
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Mecom
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Cnnm2
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Serpina3n
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Perp
all_gene_names <- rownames(dds)
all_gene_ids <- sub("_ENSMUSG.*", "", all_gene_names)
plot <- plot_genes(gene_id = gene,
gene_id_list = all_gene_ids,
deseq_obj = dds,
intgroup = params$sample_column,
plot_ggplot = TRUE,
color = sample_colors,
return_data = FALSE,
print = TRUE,
save_path = file.path(params$output_dir, "images", "gene_plots"))
Heatmaps
Cytokine treated samples
gene_lists <- openxlsx::getSheetNames(params$cytokine_treated_gene_list)
gene_file <- params$cytokine_treated_gene_list
control <- "WT_Cytokine_Treatment"
treatment <- "PTPN2_KO_Cytokine_Treatment"
heatmap_chunks <- gene_lists %>%
map(~knit_expand("src/rules/scripts/heatmap_template.Rmd"))Mitochondria related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Calcium and Potassium
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Immune response related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Beta cell related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Lipid metabolism related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Enzymes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Solute carrier proteins
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Lori green
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Lori yellow
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Lori Blue
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Non cytokine treated samples
gene_lists <- openxlsx::getSheetNames(params$untreated_gene_list)
gene_file <- params$untreated_gene_list
control <- "WT_NoTreatment"
treatment <- "PTPN2_KO_NoTreatment"
heatmap_chunks <- gene_lists %>%
map(~knit_expand("src/rules/scripts/heatmap_template.Rmd"))Mitochondria related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Immune response related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Lipid metabolism related genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Protein metabolism genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Solute carrier protein genes
Heatmap of DE genes across all samples
heatmap_1 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
color_test = sample_colors)
print(heatmap_1)
Heatmap of DE genes across only the samples used in the comparision
heatmap_2 <- make_heatmap(dds = dds,
vsd = vsd,
de_genes = all_gene_ids,
treatment = treatment,
control = control,
group = params$sample_column,
print_genenames = TRUE,
cluster_cols = FALSE,
save_heatmap = FALSE,
plot_groups = c(treatment, control),
color_test = sample_colors)
print(heatmap_2)
heatmap_dir <- file.path(params$output_dir, "images", "heatmaps")
gene_list_print <- sub(" ", "_", gene_list)
save_name <- paste0(gene_list_print, "_", treatment, "_", control, ".pdf")
pdf(file.path(params$output_dir, "images", "heatmaps", save_name),
height = figure_height, width = 10)
grid::grid.newpage()
grid::grid.draw(heatmap_1$gtable)
grid::grid.newpage()
grid::grid.draw(heatmap_2$gtable)
dev.off()Session Info
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] org.Hs.eg.db_3.12.0 AnnotationDbi_1.52.0
[3] KEGGREST_1.30.1 pathview_1.30.1
[5] DESeq2_1.30.0 SummarizedExperiment_1.20.0
[7] Biobase_2.50.0 MatrixGenerics_1.2.1
[9] matrixStats_0.58.0 GenomicRanges_1.42.0
[11] GenomeInfoDb_1.26.2 IRanges_2.24.1
[13] S4Vectors_0.28.1 BiocGenerics_0.36.0
[15] openxlsx_4.2.3 apeglm_1.12.0
[17] ashr_2.2-47 RColorBrewer_1.1-2
[19] viridis_0.5.1 viridisLite_0.3.0
[21] gprofiler2_0.2.0 devtools_2.3.2
[23] usethis_2.0.0 ggrepel_0.9.1
[25] pheatmap_1.0.12 Matrix_1.3-3
[27] forcats_0.5.1 stringr_1.4.0
[29] dplyr_1.0.3 purrr_0.3.4
[31] readr_1.4.0 tidyr_1.1.2
[33] tibble_3.0.6 ggplot2_3.3.3
[35] tidyverse_1.3.0 cowplot_1.1.1
[37] BiocManager_1.30.10 knitr_1.31
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6
[4] lazyeval_0.2.2 splines_4.0.3 BiocParallel_1.24.1
[7] digest_0.6.27 invgamma_1.1 htmltools_0.5.1.1
[10] SQUAREM_2021.1 magrittr_2.0.1 memoise_2.0.0
[13] remotes_2.3.0 Biostrings_2.58.0 annotate_1.68.0
[16] modelr_0.1.8 bdsmatrix_1.3-4 prettyunits_1.1.1
[19] colorspace_2.0-0 blob_1.2.1 rvest_0.3.6
[22] haven_2.3.1 xfun_0.27 callr_3.5.1
[25] crayon_1.4.0 RCurl_1.98-1.2 jsonlite_1.7.2
[28] graph_1.68.0 genefilter_1.72.1 survival_3.2-11
[31] glue_1.4.2 gtable_0.3.0 zlibbioc_1.36.0
[34] XVector_0.30.0 DelayedArray_0.16.1 pkgbuild_1.2.0
[37] Rgraphviz_2.34.0 scales_1.1.1 mvtnorm_1.1-1
[40] DBI_1.1.1 Rcpp_1.0.7 xtable_1.8-4
[43] emdbook_1.3.12 bit_4.0.4 truncnorm_1.0-8
[46] htmlwidgets_1.5.3 httr_1.4.2 ellipsis_0.3.1
[49] farver_2.0.3 pkgconfig_2.0.3 XML_3.99-0.5
[52] sass_0.3.1 dbplyr_2.0.0 locfit_1.5-9.4
[55] labeling_0.4.2 tidyselect_1.1.0 rlang_0.4.10
[58] munsell_0.5.0 cellranger_1.1.0 tools_4.0.3
[61] cachem_1.0.1 cli_2.3.0 generics_0.1.0
[64] RSQLite_2.2.3 broom_0.7.4 evaluate_0.14
[67] fastmap_1.1.0 yaml_2.2.1 processx_3.4.5
[70] bit64_4.0.5 fs_1.5.0 zip_2.1.1
[73] KEGGgraph_1.50.0 xml2_1.3.2 compiler_4.0.3
[76] rstudioapi_0.13 png_0.1-7 plotly_4.9.3
[79] testthat_3.0.1 reprex_1.0.0 geneplotter_1.68.0
[82] bslib_0.2.4 stringi_1.5.3 highr_0.8
[85] ps_1.5.0 desc_1.2.0 lattice_0.20-44
[88] vctrs_0.3.6 pillar_1.4.7 lifecycle_0.2.0
[91] jquerylib_0.1.3 data.table_1.13.6 bitops_1.0-6
[94] irlba_2.3.3 R6_2.5.0 gridExtra_2.3
[97] sessioninfo_1.1.1 MASS_7.3-54 assertthat_0.2.1
[100] pkgload_1.1.0 rprojroot_2.0.2 withr_2.4.1
[103] GenomeInfoDbData_1.2.4 hms_1.0.0 grid_4.0.3
[106] coda_0.19-4 rmarkdown_2.11 mixsqp_0.3-43
[109] bbmle_1.0.23.1 numDeriv_2016.8-1.1 lubridate_1.8.0 ifelse(!dir.exists(file.path(params$output_dir, "objs")),
dir.create(file.path(params$output_dir, "objs")), FALSE)[1] FALSEsaveRDS(dds, paste0(params$output_dir, "/objs/dds_obj.rda"))
saveRDS(vsd, paste0(params$output_dir, "/objs/vsd_obj.rda"))